Background and Theory ISPs ISPs, wicked problems, dynamic problems, and naturalistic decision making (NDM) all share common features. ISPs and wicked problems are both resistant to solutions; however, wicked problems, like climate change or world hunger, are often characterized as unsolvable or have an unlimited number of influential factors (Murgatroyd, 2010). ISP solving models are also similar to NDM frameworks in that they share time pressures, group constraints, and changing conditions, and require both critical and creative thinking skills. However, NDM usually assumes a level of expertise within a content domain and a level of risk—conditions that are challenging to replicate. For this study, we used the ISP solving framework because we wanted to study problems that were challenging and dynamic yet could be solved and did not heavily rely on specific domain knowledge. We felt this framework best aligned with the most common wilderness education learning contexts. Problem structuredness is one of the defining characteristics of problem types, and problems will vary from well structured to ill structured along a spectrum depending on Collins et al. 181 the rigidity of the framework in which any given problem is situated (Jonassen, 2004). ISPs possess elements that are unknown or not known with any degree of confidence (Jonassen, 2004), include multiple solutions or solution paths, and entail multiple criteria for evaluating solutions. ISPs have answers that are context relevant and context dependent (Kitchener & King, 1990) and where the goal states are vaguely defined (Jausovec, 1994). Such problems are less frequently presented in classrooms due to their highly conditional and time-consuming nature. Case analysis, design, and dilemma problems are typically ill structured (Jonassen, 2000). In contrast, well-structured problems have clearly defined boundaries and have clear and well-articulated solutions (Kitchener & King, 1990). These types of problems present all elements of a problem to the learner at the introduction to the problem (Jonassen, 2004). In solving well-structured problems, individuals apply a limited number of domain-specific rules and/or principles. These rules and/or principles are organized and predictable (Jonassen, 2004), and therefore require less cognitive complexity than ISP solving does. Well-structured problems have definitive right and wrong answers that change little over time and context (Kitchener & King, 1990). They have knowable and comprehensible solutions (Jonassen, 2004). Logical, algorithmic, and story problems are generally well structured in nature. Up until the 20th century, most thinking was regarded in this well-structured way (rational and certain, with stable outcomes; Labouvie-Vief, 2006), and as a result, many educational approaches are still designed to promote this kind of structured thinking. However, well-structured problem-solving skills learned in the classroom do not necessarily guaran.

1. Background and Theory ISPs ISPs, wicked problems, dynamic
problems, and naturalistic decision making (NDM) all share
common features. ISPs and wicked problems are both resistant
to solutions; however, wicked problems, like climate change or
world hunger, are often characterized as unsolvable or have an
unlimited number of influential factors (Murgatroyd, 2010). ISP
solving models are also similar to NDM frameworks in that they
share time pressures, group constraints, and changing
conditions, and require both critical and creative thinking skills.
However, NDM usually assumes a level of expertise within a
content domain and a level of risk—conditions that are
challenging to replicate. For this study, we used the ISP solving
framework because we wanted to study problems that were
challenging and dynamic yet could be solved and did not
heavily rely on specific domain knowledge. We felt this
framework best aligned with the most common wilderness
education learning contexts. Problem structuredness is one of
the defining characteristics of problem types, and problems will
vary from well structured to ill structured along a spectrum
depending on Collins et al. 181 the rigidity of the framework in
which any given problem is situated (Jonassen, 2004). ISPs
possess elements that are unknown or not known with any
degree of confidence (Jonassen, 2004), include multiple
solutions or solution paths, and entail multiple criteria for
evaluating solutions. ISPs have answers that are context
relevant and context dependent (Kitchener & King, 1990) and
where the goal states are vaguely defined (Jausovec, 1994).
Such problems are less frequently presented in classrooms due
to their highly conditional and time-consuming nature. Case
analysis, design, and dilemma problems are typically ill
structured (Jonassen, 2000). In contrast, well-structured
problems have clearly defined boundaries and have clear and
well-articulated solutions (Kitchener & King, 1990). These
types of problems present all elements of a problem to the

2. learner at the introduction to the problem (Jonassen, 2004). In
solving well-structured problems, individuals apply a limited
number of domain-specific rules and/or principles. These rules
and/or principles are organized and predictable (Jonassen,
2004), and therefore require less cognitive complexity than ISP
solving does. Well-structured problems have definitive right
and wrong answers that change little over time and context
(Kitchener & King, 1990). They have knowable and
comprehensible solutions (Jonassen, 2004). Logical,
algorithmic, and story problems are generally well structured in
nature. Up until the 20th century, most thinking was regarded in
this well-structured way (rational and certain, with stable
outcomes; Labouvie-Vief, 2006), and as a result, many
educational approaches are still designed to promote this kind
of structured thinking. However, well-structured problem-
solving skills learned in the classroom do not necessarily
guarantee success in solving ISPs in the real world (Choi &
Lee, 2008). ISPs require a different set of cognitive processes
and resources (see Table 1). To resolve ISPs, individuals must
use creativity, tolerance for novelty, and cognitive flexibility.
Creative thinking aids the production of multiple varying
solutions (Sternberg, Kaufman, & Pretz, 2002) and is leveraged
in the reorganization, restriction, and combination of generated
solutions (Mumford, Costanza, Threlfall, Baughman, & Reiter-
Palmon, 1993). As ISPs create cognitive disequilibration,
tolerance for novelty is needed to ensure that this
disequilibration is resolved rather than abandoned (Morra,
Gobbo, Marini, & Sheese, 2008). Finally, efficient problem
solvers will need to have enough cognitive flexibility to hold
multiple pieces of information in their working memory and
move fluidly between them (Jonassen, 2004).
Learning to Solve ISPs Like most skills, ISP solving is learned
and developed (Labouvie-Vief, 2006). The literature on learning
ISP solving skills points to three essential elements for the
development of these skills: (a) immediately relevant
environments that encourage the practice of solving actual

3. problems (Murgatroyd, 2010), (b) a change in cognitive
equilibration (Morra et al., 2008), and (c) a supportive and
collaborative learning environment (Fleming & Alexander,
2001; Johnson, 2006). When these three elements are present,
the opportunities for learning and developing ISP solving
through experience and practice are enhanced. These three
elements are inherently present in most wilderness education
programs (cf. Gookin & Leach, 2009). 182 Journal of
Experiential Education 39(2) Wilderness education programs
use outdoor settings to afford rich and novel learning
environments. The nature of these outdoor settings, routinely
shuffling priorities in response to weather and other random
dynamics, makes them somewhat unpredictable and nonlinear.
Thus, many of the problems encountered in wilderness
education have neither singular solutions nor singular solution
paths. These contexts are often novel to participants; thus,
students are less likely to engage habitual solution paths, which
promotes a change in the students’ cognitive equilibration. In
addition, the inherent small group context common in
wilderness education creates supportive and collaborative
learning groups where students and instructors live and travel
together. The instructors encourage students to collaborate in
solving problems (Gookin & Leach, 2009). Such settings urge
students to wrestle with group dynamics, objective and
subjective hazards, and the task of living in a novel
environment (Ewert & McAvoy, 2000; Hattie, Marsh, Neill, &
Richards, 1997). Although existing empirical studies have
shown that wilderness education can improve students’
perceptions of problem-solving and related skills such as
resilience and creativity (cf. Hattie et al., 1997), and a number
of theoretical papers have linked outdoor programs with
learning (naturalistic) decision making, challenging
assumptions, and questioning norms (cf. Galloway, 2002;
Gookin & Leach, 2009; McKenzie, 2003), there is very little
research on actual performance of problem-solving skills.
Emerging Adults Emerging adults (roughly aged 18-29) are

4. observed in contemporary industrialized cultures where the gap
between adolescence and adulthood has broadened (Tanner,
Arnett, & Leis, 2008). Some of the cognitive structures and
intellectual milestones that an individual needs for solving ISPs
are developed during this critical life stage, as a rapid
expansion of complex thought structures occurs (Labouvie-Vief,
2006; Tanner et al., 2008). Emerging adults encounter a period
where knowledge becomes Table 1. Ill-Structured Problem-
Solving Process. Represent the problem 1. Articulate the
problem space and contextual constraints 2. Identify and clarify
alternative opinions, positions, and perspectives Develop
solutions 3. Generate possible problem solutions Make
justifications 4. Assess the viability of alternative solutions by
constructing arguments and articulating personal beliefs 5.
Monitor the problem space and solution options (Is the problem
solvable?) Monitor and evaluate solutions 6. Implement and
monitor the solution (Will the solution work?) 7. Be willing to
adapt the solution Source. Adapted from Choi and Lee (2008)
and Jonassen (1997). Collins et al. 183 disequilibrated
(Labouvie-Vief, 2006). This helps to move these individuals
away from dualist thinking (solutions that are either right or
wrong) to relativist or “selfauthored” knowledge constructions
(where solutions can be right and wrong depending on the
context) that allow students to incorporate context as a source
of knowledge (see Perry, 1970/1999). The brain is in the critical
period for the development and reorganization of the frontal
cortex, which includes the neurological centers for reasoning
and decision making (Labouvie-Vief, 2006). Thus, the average
emerging adult has fewer, but faster, connections when it comes
to making decisions involving reasoning and judgment. Most
people also experience a peak in creative potential in their early
20s (Kaufman, Kaufman, & Lichtenberger, 2011). For these
reasons, emerging adults are primed to learn and develop ISP
skills that involve complex thinking, creativity, tolerance for
novelty and ambiguity, and cognitive flexibility. Given the
preceding literature related to ISP solving skill development, we

5. hypothesized that students would show significant gains in ISP
solving skills after completion of a wilderness expedition
compared with students engaged in a leadership curriculum in a
traditional classroom setting. Problem-solving skills were
characterized as follows: representing the problem (RP),
developing solutions (DS), making justifications (MJ),
monitoring and evaluating problem space and solutions (ME),
and identifying problemsolving stages (PS). These skills were
evaluated based on the above problem-solving literature and
similar studies from other fields (cf. Bixler, 2007; Chen, 2010).
Method Setting and Participants This study involved a
comparison group and an experimental group. The experimental
group consisted of 91 students who were enrolled in National
Outdoor Leadership School (NOLS) semester courses. NOLS
semesters are between 75 and 90 days in length and include
between 12 and 16 participants who live and travel (hiking,
kayaking, or other modes) together for the duration of the
semester. NOLS semester courses are similar in both college
credits and length to a typical university semester, allowing
adequate comparable dosage and incubation of ISP solving
process skills. NOLS semesters were chosen for this study
because they are long enough that students have sufficient
opportunities to practice a variety of problem-solving skills
under the guidance of instructors and with peer collaboration.
The comparison group was made up of 65 students who were
enrolled in classroom-based leadership courses at two midsized
public regional universities in the Southeastern United States.
This comparison group was selected because the students were
of similar ages to the treatment group and were similarly
engaged in college credit earning leadership courses. The
students in the comparison group were not engaged in extended
field experiences and were not specifically learning
problemsolving skills for ISPs. Rather, these students were
familiar with similar problem contexts (the scenarios for both
study groups are based on college leadership settings) and were
learning similar leadership content as the treatment group.

6. Thus, the comparison 184 Journal of Experiential Education
39(2) group was used to control for maturation effects in skill
development and was not intended to serve as an “alternative
treatment.” The collegiate context used for all scenarios, if
anything, offered an advantage in gains to the comparison
group, which studied on campus that semester. All participants
in this study were exposed to leadership curriculum in
nationally accredited parks and recreation courses.
Table 2. Study Sample Demographics and Summary
Information. Classroom Wilderness Full study Participants 65
91 156 Age rangea 19-29 16-32 16-32 Age averagea 21 21 21
Females 50 30 80 Males 15 61 76 Average years of schoola 14
14 14 Pretest—Scenario A 44 47 91 Pretest—Scenario B 21 44
65 a Numbers reported in years, all others are raw counts. Table
3. Means and Standard Errors for ISP Subscales at Pre- and
Posttesting. Subscale n M (SE) Pretest M (SE) Posttest Grand
mean RP (classroom) 65 2.75 (1.38) 2.68 (1.95) 3.43 RP
(wilderness) 91 3.26 (1.82) 4.69 (2.02) DS (classroom) 65 3.48
(1.11) 3.11 (0.94) 3.89 DS (wilderness) 91 3.53 (1.36) 5.15
(1.30) MJ (classroom) 65 0.91 (1.04) 0.75 (1.03) 1.28 MJ
(wilderness) 91 0.73 (1.04) 2.49 (1.96) ME (classroom) 65 2.45
(1.03) 2.00 (0.92) 2.64 ME (wilderness) 91 2.30 (1.02) 3.63
(1.58) PS (classroom) 65 1.72 (1.07) 1.85 (1.08) 2.08 PS
(wilderness) 91 1.62 (1.29) 2.92 (1.68) Note. ISP = ill-
structured problem; RP = Representing the Problem; DS =
Developing
Solution
s; MJ = Making Justifications; ME = Monitoring and
Evaluating; PS = Problem-Solving Stages. Discussion This
study found that students who participated in wilderness

7. education showed significant growth in their problem-solving
skills at the conclusion of their semester-long experience as
compared with peers engaged in a leadership curriculum in a
more traditional classroom setting. A survey of the literature
finds that wilderness education and adventure education
experiences lead to a variety of technical, intrapersonal, and
interpersonal outcomes (Hattie et al., 1997). In addition, studies
have shown that the intentional scaffolding of problems for
students can help in their skill development in problem solving
(Bixler, 2007; Ge, 2001) and that outdoor programs specifically
can have a positive influence on general problem-solving
abilities (Viadero, 1997). However, few studies have explored
the development of discrete problem-solving skills in a
wilderness education context. We posit that wilderness
education is an 188 Journal of Experiential Education 39(2)
Table 4. Summary of Significant Interaction (Group × Time)
Effect Sizes by Subscale. Measure F(1, 149) p Partial η2 RP
17.63 <.001 0.106 DS 53.99 <.001 0.266 MJ 30.87 <.001 0.172
ME 62.08 <.001 0.294 PS 26.73 <.001 0.152 Note. RP =
Representing the Problem; DS = Developing